Concatenating and Appending Data

It's **Day 112**. In SQL, you had `UNION`. In Pandas, we have `pd.concat()`.

Vertical Concatenation (Stacking)

Use this when you have the same columns but different rows (e.g., "Sales January" and "Sales February").

# Stack them on top of each other

full_year = pd.concat([jan_df, feb_df], axis=0)

Use this when you have the same rows but different columns for both.

# Glue them side-by-side

combined = pd.concat([data_df, metadata_df], axis=1)

When you concat, what happens if one table has a column the other doesn't?

**join='outer'** (Default): Includes all columns, fills gaps with `NaN`.

**join='inner'**: Only includes columns that exist in BOTH tables.

Create two small DataFrames with slightly different columns and use `pd.concat` with both `inner` and `outer` joins to see the difference.

*Day 113: Reshaping Data with Pivot and Melt.*